首页|结合极化白化滤波和SimSD-CapsuleNet的PolSAR图像配准

结合极化白化滤波和SimSD-CapsuleNet的PolSAR图像配准

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极化合成孔径雷达(PolSAR)图像配准在地物分类、变化检测、图像融合中都具有广泛应用.现有的PolSAR图像配准方法,无论是基于深度学习还是传统方法,大多采用PolSAR幅度影像信息进行处理.这种处理方式导致大量极化信息丢失,同时在PolSAR图像固有相干斑噪声影响下,配准精度和可靠性表现不佳.为此,本文首先发展了一种有效的基于极化白化滤波(PWF)精细化处理的关键点检测器,利用PWF对PolSAR图像进行相干斑噪声抑制,通过阈值约束、形态学腐蚀及非极大值抑制来选取显著且分布均匀的匹配关键点.进一步地,本文设计了一种孪生简单稠密胶囊网络(SimSD-CapsuleNet)来快速提取PolSAR图像的浅层纹理特征和深层语义特征,同时为了充分利用极化信息,本文将极化协方差矩阵作为输入数据.本文计算了胶囊形式特征描述符之间的距离,并将其输入硬L2损失函数用于模型的训练.本文方法在不同传感器获取的不同分辨率PolSAR图像上进行验证.结果表明,该方法能够在更短的时间内获取更加均匀且数量更多的匹配关键点,结合PWF和深度神经网络可以实现快速准确的PolSAR图像配准.
PolSAR image registration combining polarization whitening filtering and SimSD-CapsuleNet
Polarimetric synthetic aperture radar(PolSAR)image registration has a wide range of applications in feature classifi-cation,change detection,and image fusion.Existing PolSAR image registration methods,whether based on deep learning or conventional methods,use PolSAR magnitude image information for processing.This processing leads to a large amount of po-larization information loss,and at the same time,the registration accuracy and reliability perform poorly under the influence of the inherent coherent speckle noise of PolSAR images.To this end,this paper first develops a novel and effective key point de-tector based on polarization whitening filter(PWF)refinement processing,which uses PWF to suppress coherent speckle noise in PolSAR images and selects significant and uniformly distributed matching key points by threshold constraint,morphological erosion,and non-extreme value suppression.Further,in this paper,we design a Siamese simple dense capsule network(SimSD-CapsuleNet)to quickly extract the shallow texture features and deep semantic features of the data,and we use the po-larization covariance matrix as the input data in order to make full use of the polarization information.In this paper,the dis-tances between the capsule form feature descriptors are calculated and fed into a hard L2 loss function for the training of the model.The method in this paper is validated on PolSAR images acquired by different sensors with different resolutions,and the results show that the method can acquire more uniform and a larger number of matching key points in a shorter time,and the combination of PWF and deep neural network can achieve fast and accurate PolSAR image registration.

polarimetric synthetic aperture radarPWFCapsuleNetPolSAR image registrationpolarization covariance ma-trix

项德良、丁怀跃、管冬冬、程建达、孙晓坤

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北京化工大学信息科学与技术学院,北京 100029

北京化工大学软物质科学与工程高精尖创新中心,北京 100029

火箭军工程大学作战保障学院,陕西西安 710025

临沂大学自动化与电气工程学院,山东临沂 276000

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极化合成孔径雷达 极化白化滤波器 胶囊网络 PolSAR图像配准 极化协方差矩阵

国家自然科学基金

62171015

2024

测绘学报
中国测绘学会

测绘学报

CSTPCD北大核心
影响因子:1.602
ISSN:1001-1595
年,卷(期):2024.53(3)
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